Analysis of incremental augmented affine projection algorithm for distributed estimation of complex signals

TL;DR

This paper introduces an incremental augmented affine projection algorithm for distributed estimation of complex signals, leveraging second order statistical information and spatio-temporal diversity to enhance performance in networked systems.

Contribution

It proposes a novel incAAPA algorithm that processes both proper and improper signals, with derived performance metrics and convergence conditions for distributed complex signal estimation.

Findings

The incAAPA algorithm outperforms existing methods in simulations.

It effectively estimates both proper and improper complex signals.

Theoretical analysis confirms convergence and steady-state performance.

Abstract

This paper considers the problem of distributed estimation in an incremental network when the measurements taken by the node follow a widely linear model. The proposed algorithm which we refer to it as incremental augmented affine projection algorithm (incAAPA) utilizes the full second order statistical information in the complex domain. Moreover, it exploits spatio-temporal diversity to improve the estimation performance. We derive steady-state performance metric of the incAAPA in terms of the mean-square deviation (MSD). We further derive sufficient conditions to ensure mean-square convergence. Our analysis illustrate that the proposed algorithm is able to process both second order circular (proper) and noncircular (improper) signals. The validity of the theoretical results and the good performance of the proposed algorithm are demonstrated by several computer simulations.